Using extend simulation tool to study the logistic requirements of the standing contingency task force
Bibliographic record
Abstract
The goal of this research is twofold. First to discover and develop a tool that would enable the analysis of the deployed support requirements of the Canadian Forces (CF) new concept of a rapidly deployable, integrated, expeditionary amphibious force and second to use these tools to provide insights into the proposed options. This research follows the approach described by Averil M. Law and W. David Kelton with regards to the steps conducted in a simulation study. These steps include the continuing determination of the validity of the model/simulation to include the data required as an input and the output data as generated by the simulation. Wherever possible subject matter experts and current CF manuals will be used to access valid data. In order to validate if the model/simulation is working correctly a series of tests will be conducted and subject matter experts to discuss whether the model/simulation outputs where within expected values. Once the model/simulation has been validated it will be used to conduct experiments into the supportability of the various proposed options for the Standing Contingency Task Force.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".